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import torch |
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import gradio as gr |
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from src.load_html import get_description_html |
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from src.audio_processor import AudioProcessor |
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from src.model.behaviour_model import get_behaviour_model |
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from transformers import ( |
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pipeline, |
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WavLMForSequenceClassification |
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) |
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def create_demo(): |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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segmentation_model = pipeline( |
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task="automatic-speech-recognition", |
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model="openai/whisper-large-v3-turbo", |
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tokenizer="openai/whisper-large-v3-turbo", |
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device=device |
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) |
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emotion_model = WavLMForSequenceClassification.from_pretrained("links-ads/kk-speech-emotion-recognition") |
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emotion_model.to(device) |
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emotion_model.eval() |
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behaviour_model = get_behaviour_model( |
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classifier_weights_path="src/model/classifier_weights.bin", |
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device=device, |
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) |
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audio_processor = AudioProcessor( |
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emotion_model=emotion_model, |
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segmentation_model=segmentation_model, |
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device=device, |
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behaviour_model=behaviour_model, |
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) |
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with gr.Blocks() as demo: |
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gr.HTML(get_description_html) |
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audio_input = gr.Audio(label="Upload Audio", type="filepath") |
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submit_button = gr.Button("Generate Graph") |
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graph_output = gr.Plot(label="Generated Graph") |
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submit_button.click( |
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fn=audio_processor, |
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inputs=audio_input, |
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outputs=graph_output |
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) |
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return demo |
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if __name__ == "__main__": |
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demo = create_demo() |
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demo.launch(show_api=False) |
